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Within panel_main.mat there are 3 separate cell structures. 
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BCFF_bp_merged_dummy  �  Blue Chip Financial (merged participants & FWD_Guidance/QE/MEP events dummy)
BLFF_dummy            �  Bloomberg Financial Aligned (FWD_Guidance/QE/MEP events dummy)
dummySet              -  Manually aligned FWD_Guidance/QE/MEP events


The dummySet:
-Only ever has zeros appended to it to equal the length of the bluechip survey dates (fixed effects are all hard coded to dates starting in 2001 to current)



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------------------- The 2 "data" structures have nearly identical format: -------------------
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For the BCFF datastructure:
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	BCFF_bp.dtnum � the datenum of the bluechip survey (mm/01/yyyy)	
	BCFF_bp.cQ - read as row vectors, the datenums for the quarters forecasted by bluechip (convention used - 03/01 (Q1) 06/01 (Q2) 09/01 (Q3) 12/01 (Q4).
	-These are used to from one row to the next to compute if there has been a change in forecasted quarters
	BCFF_bp.shiftQ � the dummy for whether or not there has been a change in forecasted quarters since last survey
	BCFF_bp.fgQEnone - the dummy for forward guidance with no QE at the time of announcement/event
	BCFF_bp.fgQE1 - the dummy for forward guidance with QE1 at the time of announcement/event
	BCFF_bp.fgQE3 - the dummy for forward guidance with QE3 at the time of announcement/event
	BCFF_bp.otherQEnone - the dummy for other programs (MEP,etc.) with no QE at the time of announcement/event
	BCFF_bp.otherQE12 - the dummy for other programs (MEP,etc.) with QE1 or QE2 at the time of announcement/event
	BCFF_bp.partArray � An ordered list of strings with rows matching the BCFF.fcasters.f_<#> "participant #."  The strings served as unique IDs and were thrown through a merging algorithm to ensure all participants were correctly matched to their forecasts over the 10Yr history.
	BCFFv.partArray_all � Shows the different unique strings that have been matched as the same forecasting participant.  
{i.e.'banc of america securities, llc';
'banc of america securities';
'banc of amercia securities';
'banc of america-merrill lynch';
'bank of america merrill lynch';
'bank of america';
'bank of america-merrill lynch';
 }
	
BCFF_bp.('indicator').f_<#> � the forecast value/s for the indicator from participant <#>
	-there are 6 forecast values in the bluechip reports (6 Quarter horizon), and a single forecasted value for each bloomberg survey indicator (may not want these)
BCFF_bp.('indicator').cons  - the consensus BlueChip forecast (MEAN of participants)	
	



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For the BLFF datastructure:
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	BLFF.dtnum � the date of the bluechip survey "release date" [num is always first of the month (mm/01/yyyy)]
	BLFF.'indicator'.dtrelnum - the bloomberg release date for a given indicator, aligned to upcoming bluechip "release date"
	
	BLFF.dtcol � the date of the bluechip survey "last collection date" [col is somewhere between (mm/19-26/yyyy)]
	BLFF.'indicator'.dtrelcol - the bloomberg release date for a given indicator, aligned to upcoming bluechip "last collection date"


	BLFF.'indicator'.dtrelnum - the bloomberg "indicator release date" with Blue Chip "release date" alignment
	BLFF.'indicator'.forecastnum - the bloomberg indicator forecast with "release date" alignment
	BLFF.'indicator'.actualnum - the bloomberg forecast with "release date" alignment

	BLFF.'indicator'.dtrelcol - the bloomberg "indicator release date" with Blue Chip "last collection date" alignment
	BLFF.'indicator'.forecastcol - the bloomberg indicator forecast with "last collection date" alignment
	BLFF.'indicator'.actualcol - the bloomberg forecast with "last collection date" alignment


	For daily, end of day data, we take the inter-survey window to run from the begining of the previous survey to the end of the current survey.
	(i.e. prev survey == '03/22-23/2015', current survey == '4/25-26/2015', the window would be 3/22-4/26/2015) -- This window of values is aligned to show the conditional data up to a release (dtcol would appear as 4/26/2015 in the hypothetical situation just used)
	NaN's will appear at the start of all these vectors of % return/difference, because prior to the 1st daily data available there is often no prior survey date to construct a return window (and do not construct an artificial 1 month/30 day window)
	BLFF.'indicator'.actualcolWindow - change in the inter-survey bluechip collection dates
	BLFF.'indicator'.actualcolEvents - 2 day - change over the FOMC/other events in the sample
	BLFF.'indicator'.actualcolWsubE - change in the inter-survey bluechip collection dates, leaving out the contribution of the 2 day event
	
	* note: if there is more than 1 event in a particular survey window, the difference is summed together (this results in events for 16/170 survey windows.  There are 20 total events).  
	* If a 2day event were to take place in the last day of the survey, only the 1 day falling inside the survey window would be counted in the net event difference in the period.  This does not appear to occur.

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General notes:
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*Pulled bloomberg actual values back to 2000.
*Have bluechip survey results back to 2001.
*The BCFF and BLFF dtnum goes from 01/2001-03/2015








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